Closed-loop model validation based on the two-model divergence method

This paper is concerned with model validation and detection of parameter changes under closed-loop conditions. Two closed-loop model validation algorithms are developed based on the two-model divergence method. The algorithms are only sensitive to the plant changes that affect closed-loop performance. The first algorithm is sensitive to changes in both plant and disturbance dynamics. A novel feature of the second algorithm is that it is only sensitive to the changes in plant dynamics, regardless of changes in disturbance dynamics. Under certain conditions, the proposed algorithms can also be applied for fault detection, e.g. detection of actuator or sensor fault, etc. The developed algorithms are evaluated by simulations as well as experimental applications on a pilot scale process.